Neural-Fuzzy for Econometrics Estimation

In the current book there exists a practical guideline, which is most referenced by Mathwork (MatLab userguide software 2001 version), useful for economists and students of economics in order to apply Neural-Fuzzy approach in econometrics estimation in a simple way.

Autorentext

Arshia Amiri is a PhD student in Inserm U912, University de la Mediterrane, Marseille, France, and Shiraz University, Shiraz, Iran. He is the author of several articles referenced by Elsevier, Econlit and RePec in France, Germany and US, and is the president of Marseille Health Economics Committee in France.

Klappentext

In recent years, Neural-Fuzzy methods have received an increasing amount of attention among macroeconomic forecasters because of their potential to detect and reproduce linear and nonlinear relationships among a set of variables. This paper provides a highly accessible introduction to Neural-Fuzzy methods and establishes several parallels with standard econometrics techniques. The idea of this book came to explain of using a completely new approach of artificial intelligence, to include neural networks, fuzzy logic, or combined neural-fuzzy logic to analyzing the subject of econometrics investigations. In the current book there exists a practical guideline, which is most referenced by Mathwork (MatLab userguide software 2009 version), useful for economists in order to apply Neural-Fuzzy approach in econometrics estimation in a simple way.